SAP HANA · Lesson

Architecture

Quick answer

By collapsing the old transactional/analytical split, HANA let SAP remove redundant aggregate and index tables, simplify the data model (e.

Key takeaways

HANA stores data primarily in memory (persisted to disk for durability) and, for most application data, in a column store.
Column store: default for application/analytical data; fast scans, aggregation, compression.
Row store: for a few technical/frequently-single-row-accessed tables.
Watch out: Assuming HANA is just a faster disk DB, it is architecturally different.

The in-memory, columnar core

HANA stores data primarily in memory (persisted to disk for durability) and, for most application data, in a column store. In-memory access removes disk-I/O latency, and columnar storage makes scans and aggregations extremely fast and highly compressible, so analytics can run directly on live transactional data.

Row vs column store

Column store: default for application/analytical data; fast scans, aggregation, compression.
Row store: for a few technical/frequently-single-row-accessed tables.

Key architectural pieces

Index server: the main engine (SQL, calculation, stores).
Persistence layer: data volumes + log volumes for durability and recovery.
Multitenant database containers (MDC): a system DB plus isolated tenant DBs.

Why it changed SAP

By collapsing the old transactional/analytical split, HANA let SAP remove redundant aggregate and index tables, simplify the data model (e.g. the universal journal), and deliver real-time analytics, the foundation of S/4HANA.

Common pitfalls

Assuming HANA is just a faster disk DB, it is architecturally different.
Ignoring memory sizing, data must fit in RAM.
Overlooking persistence, in-memory still needs durable storage.

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Check your understanding

Which statement is true of Architecture?
- A. In-memory access removes disk-I/O latency, and columnar storage makes scans and aggregations extremely fast…
- B. Good configuration practice keeps customizing aligned to standard, well-documented, and maintainable, so the…
- C. These CDS examples show Core Data Services views, the modern SAP modeling layer, with joins, associations…
Show answer
A. In-memory access removes disk-I/O latency, and columnar storage makes scans and aggregations extremely fast…
Covered in the “The in-memory, columnar core” section of this lesson.
Which of these also applies to Architecture?
- A. Hunting through menus instead of using TCodes.
- B. g.
- C. Building custom apps when a standard one exists.
Show answer
B. g.
Covered in the “Why it changed SAP” section of this lesson.
Which part of the Learn SAP curriculum covers Architecture?
- A. SAP how-to guides
- B. SAP PP
- C. SAP HANA
Show answer
C. SAP HANA
This lesson sits in the SAP HANA section of the Learn SAP course.

Frequently asked questions

What does Architecture mean in SAP HANA?

SAP HANA is an in-memory, column-oriented database that keeps data in RAM and processes transactions and analytics on the same data. Its architecture is what makes S/4HANA’s real-time capabilities and simplified data model possible.

What is worth remembering about Architecture in practice?

By collapsing the old transactional/analytical split, HANA let SAP remove redundant aggregate and index tables, simplify the data model (e.

What is another point to note about Architecture?

HANA stores data primarily in memory (persisted to disk for durability) and, for most application data, in a column store.

What tends to go wrong with Architecture in SAP HANA?

Assuming HANA is just a faster disk DB, it is architecturally different. Ignoring memory sizing, data must fit in RAM. Overlooking persistence, in-memory still needs durable storage.